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Explainer

AI Learning Methods — Plain English Guide (2026)

There are about six ways AI actually learns. Every modern LLM and agent uses a mix of them. Here's what each one is, what it's good for, and where you'll meet it in the wild.

1. Supervised learning

The model trains on labeled examples: input → correct output. The classic recipe for spam classifiers, sentiment models, image tagging. It needs labeled data, which is expensive at scale — that's why pure supervised learning isn't how foundation models are pre-trained, but it's how they're fine-tuned for narrow tasks.

Where you'll see it: Fine-tuning an LLM on your support tickets, intent classifiers, image labeling.

2. Unsupervised learning

No labels. The model finds structure on its own — clusters, anomalies, embeddings, dimensionality reduction. Less of a headline today but still core for grouping unknown data, fraud anomaly detection, and building embeddings used in RAG.

Where you'll see it: Clustering customers, anomaly detection, building vector embeddings for search.

3. Self-supervised learning

The trick that made modern LLMs possible. The model invents its own labels from raw data — predict the next token, fill in the masked word, contrast a matching image-caption pair. Huge unlabeled corpora become trainable, and the model picks up structure that transfers everywhere.

Where you'll see it: Pre-training every modern LLM (GPT, Claude, Gemini, Llama) and embedding models.

4. Reinforcement learning (RL)

The model interacts with an environment and learns from a reward signal. Used in robotics, game-playing, and increasingly in agent training where the 'environment' is a set of tools and the 'reward' is task success. RL is sample-hungry and unstable, but it's how you teach a model to choose actions, not just predict text.

Where you'll see it: Game-playing agents, robotic control, training planners inside AI agents.

5. Reinforcement learning from human feedback (RLHF)

RL with a learned reward model that approximates human preference. The standard alignment recipe for instruction-tuned chat models: humans rank outputs, a reward model learns to predict their rankings, the LLM is fine-tuned to produce highly-ranked outputs. Recent variants (DPO, KTO) skip the explicit reward model but keep the same idea.

Where you'll see it: Turning a raw pre-trained LLM into a helpful, instruction-following assistant.

6. Transfer learning & fine-tuning

Not a learning paradigm so much as a workflow: take a model trained on a huge generic corpus and continue training on your narrow data. Cheaper than training from scratch and the foundation of every domain-specific LLM project today.

Where you'll see it: Domain-specific assistants, classifiers built on a base LLM, LoRA adapters.

Go beyond the explainer

The LLM Engineering diploma teaches you to actually apply these methods — fine-tuning, RLHF basics, evaluation, RAG, deployment. The AI Agents Mastery diploma covers the RL and tool-use side for autonomous systems. Both are free end-to-end.

Open LLM Engineering Open AI Agents Mastery

Common questions

Which AI learning method is used in ChatGPT?

A stack of three. Self-supervised pre-training on huge text corpora gives the base model its world knowledge. Supervised fine-tuning teaches it the instruction-following format. RLHF (or DPO) tunes it to prefer helpful, harmless, honest outputs.

Do I need to understand all of these to build AI products?

You need a conceptual grip on all of them, plus working skill in fine-tuning, RAG, and evaluation. That's exactly the scope of the LLM Engineering diploma.

Is reinforcement learning still relevant if RLHF is the standard?

Yes — RL is making a comeback for AI agents, where the 'reward' is task success across multi-step tool use. RLAIF, process reward models, and step-level RL are active areas in 2026.

What's the difference between unsupervised and self-supervised learning?

Unsupervised learning has no target at all (clustering, anomaly detection). Self-supervised invents a target from the input itself (next-token prediction, masked language modelling) — so the model has something concrete to optimise toward.

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